Backend Engineer, Enterprise AI

Skills this role screens for

About the role

About Turing

Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com.

Role: Backend Engineer, Enterprise AI

Location: New York City, Madison Square Park area

Reports to: Head of Engineering, Enterprise AI

Experience Level: 6+ years

Work Model: Hybrid, with regular in-office presence

What you get

Why Join Enterprise AI?

Enterprise AI is building the platform that enables enterprises to deploy and operate AI systems in production.

As a Backend Engineer, you will build the services and APIs behind the platform, including agent orchestration, model routing, enterprise integrations, identity and permissions, evaluation systems, and usage metering.

You will help harden the platform for multi-tenant, enterprise-grade operation while building features that allow customers to configure and operate AI systems with greater independence.

As a Backend Engineer, You Will

Build Core Platform Services

Design, build, and operate backend services and APIs that support agent orchestration, model routing, enterprise integrations, permissions, evaluation, and usage metering.

Build Reliable LLM Applications

Develop production systems using tool use, agent loops, retrieval, prompt versioning, and agent versioning.

Enable Enterprise Integrations

Build and maintain integrations with document stores, ticketing systems, data platforms, and other enterprise systems.

Build Secure Multi-Tenant Systems

Design data models, identity systems, permissions, and service boundaries that support secure operation across multiple tenants.

Own Features End to End

Take features from architecture through implementation, testing, deployment, observability, and ongoing operation.

Build for Reuse and Scale

Create connectors, services, and agent capabilities that can be reused across customers rather than rebuilt for each deployment.

The Anti-Sell

Production engineering, not prototyping: This is not a role for someone whose experience is limited to notebooks, demos, or early prototypes. You must have owned production software.

Backend depth: You will work on services, APIs, asynchronous job systems, identity, permissions, and integrations.

Enterprise complexity: The platform must support multi-tenant operation, enterprise identity systems, customer data permissions, and complex integrations.

End-to-end ownership: You will be expected to own meaningful backend product surfaces from design through production operation.

Our Backend Engineers Typically Have

Production backend experience: Six or more years as a backend or full-stack engineer working on production SaaS or enterprise platforms.

Strong services engineering: Deep experience with Python or TypeScript services, API design, asynchronous job systems, and production operations.

Enterprise identity and permissions: Hands-on experience with OAuth, OIDC, SAML, or SCIM, as well as multi-tenant data modeling and access control.

LLM application experience: Experience building production LLM features involving tool use, agent loops, retrieval, prompt versioning, or agent versioning.

Enterprise integration experience: Experience integrating with document stores, ticketing systems, data platforms, or other enterprise systems.

Production LLM ownership: A track record of shipping LLM features used by paying customers and owning production systems after launch.

Candidates whose experience is limited to notebooks or prototypes, or who have no multi-tenant or production ownership experience, will not be a fit for this role.

Also

Top-Tier Candidates Will Have

• Experience working on agent framework internals.
• Experience implementing SOC 2 or similar controls.
• Experience building integrations for financial services or other regulated industries.
• Experience building on OpenAI or Anthropic APIs in a production environment.

Also

Values

• We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value.
• We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection
• We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.

Also

Advantages of joining Turing

Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

For applicants from the European Union, please review Turing's GDPR notice here.

• Work at the frontier of AI, helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks.
• Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS.
• Bring frontier AI innovation to the enterprise, applying lessons learned from leading AI labs to solve real-world business challenges.
• Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies.
• Move at the pace of AI innovation, with the speed, ownership, and impact of a startup.